ABSTRACT
The cognitive model we started from comes from a psychological decision theory model,
the Moving Basis Heuristics (MBH) (Barthélemy & Mullet, 1986). MBH is aimed to
implement the bounded rationality of a Decision Maker (DM) as introduced by Simon
(1969). It combines three basic principles. According to the parsimony principle, the DM
extracts some subsets from a data set whose size is small enough to be compatible with
human short-range abilities and with human computational abilities. The reliability
principle states that the DM extracts from the data a subset large enough and composes
values in such a way as to appear meaningful and to lead to reliable decisions. According
to the decidability principle, the DM allows himself to change criterion if the current
criterion does not lead to a decision (see (Barthélemy & Mullet, 1986) for details). We
add a fourth principle to make our network achieve stable decisions: the resonance
principle states that a decision made on an object is performed by a resonance between
this object and an expectancy of what this decision should be. Here, resonance refers to
of Grossberg ‘s Adaptive Resonance Theory (Carpenter & Grossberg, 1987).
